Dendrimers as nanoscale vectors: Unlocking the bars of cancer therapy.

Chemotherapy is the first choice in the treatment of cancer and is always preferred to other approaches such as radiation and surgery, but it has never met the need of patients for a safe and effective drug. Therefore, new advances in cancer treatment are now needed to reduce the side effects and burdens associated with chemotherapy for cancer patients. Targeted treatment using nanotechnology are now being actively explored as they could effectively deliver therapeutic agents to tumor cells without affecting normal cells. Dendrimers are promising nanocarriers with distinct physiochemical properties that have received considerable attention in cancer therapy studies, which is partly due to the numerous functional groups on their surface. In this review, we discuss the progress of different types of dendrimers as delivery systems in cancer therapy, focusing on the challenges, opportunities, and functionalities of the polymeric molecules. The paper also reviews the various role of dendrimers in their entry into cells via endocytosis, as well as the molecular and inflammatory pathways in cancer. In addition, various dendrimers-based drug delivery (e.g., pH-responsive, enzyme-responsive, redox-responsive, thermo-responsive, etc.) and lipid-, amino acid-, polymer- and nanoparticle-based modifications for gene delivery, as well as co-delivery of drugs and genes in cancer therapy with dendrimers, are presented. Finally, biosafety concerns and issues hindering the transition of dendrimers from research to the clinic are discussed to shed light on their clinical applications.

Rendezvous in time: an attention-based temporal fusion approach for surgical triplet recognition.

PURPOSE: One of the recent advances in surgical AI is the recognition of surgical activities as triplets of [Formula: see text]instrument, verb, target[Formula: see text]. Albeit providing detailed information for computer-assisted intervention, current triplet recognition approaches rely only on single-frame features. Exploiting the temporal cues from earlier frames would improve the recognition of surgical action triplets from videos. METHODS: In this paper, we propose Rendezvous in Time (RiT)-a deep learning model that extends the state-of-the-art model, Rendezvous, with temporal modeling. Focusing more on the verbs, our RiT explores the connectedness of current and past frames to learn temporal attention-based features for enhanced triplet recognition. RESULTS: We validate our proposal on the challenging surgical triplet dataset, CholecT45, demonstrating an improved recognition of the verb and triplet along with other interactions involving the verb such as [Formula: see text]instrument, verb[Formula: see text]. Qualitative results show that the RiT produces smoother predictions for most triplet instances than the state-of-the-arts. CONCLUSION: We present a novel attention-based approach that leverages the temporal fusion of video frames to model the evolution of surgical actions and exploit their benefits for surgical triplet recognition.

Toyota Smarthome Untrimmed: Real-World Untrimmed Videos for Activity Detection.

Designing activity detection systems that can be successfully deployed in daily-living environments requires datasets that pose the challenges typical of real-world scenarios. In this paper, we introduce a new untrimmed daily-living dataset that features several real-world challenges: Toyota Smarthome Untrimmed (TSU). TSU contains a wide variety of activities performed in a spontaneous manner. The dataset contains dense annotations including elementary, composite activities and activities involving interactions with objects. We provide an analysis of the real-world challenges featured by our dataset, highlighting the open issues for detection algorithms. We show that current state-of-the-art methods fail to achieve satisfactory performance on the TSU dataset. Therefore, we propose a new baseline method for activity detection to tackle the novel challenges provided by our dataset. This method leverages one modality (i.e. optic flow) to generate the attention weights to guide another modality (i.e RGB) to better detect the activity boundaries. This is particularly beneficial to detect activities characterized by high temporal variance. We show that the method we propose outperforms state-of-the-art methods on TSU and on another popular challenging dataset, Charades.

Enhancing the Performance of Piezoelectric Wind Energy Harvester Using Curve-Shaped Attachments on the Bluff Body.

This paper presents a piezoelectric wind energy harvester that operates by a galloping mechanism with different shaped attachments attached to a bluff body. A comparison is made between harvesters that consist of different shaped attachments on a bluff body; these include triangular, circular, square, Y-shaped, and curve-shaped attachments. Simulation of the pressure field and the velocity field variation around the different shaped bluff bodies is performed and it is found that a high pressure difference creates a high lift force on the bluff body with curve-shaped attachments. A theoretical model based on a galloping mechanism is presented, which is verified by experiments. It is observed that the proposed harvester with curve-shaped attachments provides the best performance, where the harvester with a curve-shaped attachments provides the highest voltage and power output compared to the other shaped harvesters examined in this study. This paper provides a new concept for improving the power performance of the piezoelectric wind energy harvesters with modifications made on the bluff body.

Dissecting self-supervised learning methods for surgical computer vision.

The field of surgical computer vision has undergone considerable breakthroughs in recent years with the rising popularity of deep neural network-based methods. However, standard fully-supervised approaches for training such models require vast amounts of annotated data, imposing a prohibitively high cost; especially in the clinical domain. Self-Supervised Learning (SSL) methods, which have begun to gain traction in the general computer vision community, represent a potential solution to these annotation costs, allowing to learn useful representations from only unlabeled data. Still, the effectiveness of SSL methods in more complex and impactful domains, such as medicine and surgery, remains limited and unexplored. In this work, we address this critical need by investigating four state-of-the-art SSL methods (MoCo v2, SimCLR, DINO, SwAV) in the context of surgical computer vision. We present an extensive analysis of the performance of these methods on the Cholec80 dataset for two fundamental and popular tasks in surgical context understanding, phase recognition and tool presence detection. We examine their parameterization, then their behavior with respect to training data quantities in semi-supervised settings. Correct transfer of these methods to surgery, as described and conducted in this work, leads to substantial performance gains over generic uses of SSL - up to 7.4% on phase recognition and 20% on tool presence detection - as well as state-of-the-art semi-supervised phase recognition approaches by up to 14%. Further results obtained on a highly diverse selection of surgical datasets exhibit strong generalization properties. The code is available at https://github.com/CAMMA-public/SelfSupSurg.

CholecTriplet2022: Show me a tool and tell me the triplet - An endoscopic vision challenge for surgical action triplet detection.

Formalizing surgical activities as triplets of the used instruments, actions performed, and target anatomies is becoming a gold standard approach for surgical activity modeling. The benefit is that this formalization helps to obtain a more detailed understanding of tool-tissue interaction which can be used to develop better Artificial Intelligence assistance for image-guided surgery. Earlier efforts and the CholecTriplet challenge introduced in 2021 have put together techniques aimed at recognizing these triplets from surgical footage. Estimating also the spatial locations of the triplets would offer a more precise intraoperative context-aware decision support for computer-assisted intervention. This paper presents the CholecTriplet2022 challenge, which extends surgical action triplet modeling from recognition to detection. It includes weakly-supervised bounding box localization of every visible surgical instrument (or tool), as the key actors, and the modeling of each tool-activity in the form of triplet. The paper describes a baseline method and 10 new deep learning algorithms presented at the challenge to solve the task. It also provides thorough methodological comparisons of the methods, an in-depth analysis of the obtained results across multiple metrics, visual and procedural challenges; their significance, and useful insights for future research directions and applications in surgery.

Antimicrobial textile: recent developments and functional perspective.

Antimicrobial textiles are functionally active textiles, which may kill the microorganisms or inhibit their growth. The present article explores the applications of different synthetic and natural antimicrobial compounds used to prepare antimicrobial textiles. Different types of antimicrobial textiles including: antibacterial, antifungal and antiviral have also been discussed. Different strategies and methods used for the detection of a textile's antimicrobial properties against bacterial and fungal pathogens as well as viral particles have also been highlighted. These antimicrobial textiles are used in a variety of applications ranging from households to commercial including air filters, food packaging, health care, hygiene, medical, sportswear, storage, ventilation and water purification systems. Public awareness on antimicrobial textiles and growth in commercial opportunities has been observed during past few years. Not only antimicrobial properties, but its durability along with the color, prints and designing are also important for fashionable clothing; thus, many commercial brands are now focusing on such type of materials. Overall, this article summarizes the scientific aspect dealing with different fabrics including natural or synthetic antimicrobial agents along with their current functional perspective and future opportunities.

CYP2C9 Variations and Their Pharmacogenetic Implications Among Diverse South Asian Populations.

INTRODUCTION: Allelic frequency distribution of drug metabolizing enzyme genes among populations is important to identify risk groups for adverse drug reaction and to select representative populations for clinical trials. Although India emerged as an important hub for clinical trials, information about the pharmacogenetic diversity for this region is still lacking. Here, we investigated genetic diversity of cytochrome-P450-2C9 (CYP2C9) gene which metabolizes wide range of drugs and is highly expressed in the human liver. METHODS: In total, 1278 individuals from 36 diverse Indian populations, 210 individuals from in-house data-repository and 489 other South Asian samples from the 1000 Genomes Project were selected. Variants observed in CYP2C9 gene were subjected to various statistical analyses. RESULTS: High frequency of CYP2C9*3 (~13%) and CYP2C9*3/*3 (~1%) was observed among South Asians, compared to 21 populations living outside the Indian subcontinent. The allelic/genotypic frequency does not correlate with geographical location or linguistic affiliation, except populations speaking Tibeto-Burmans language, who have lower frequency of CYP2C9*3 and CYP2C9*3/*3. Since, South Asians practice strict endogamy, presence of unique mutation and high frequency of homozygous genotypes not surprising. CYP2C9*3 has been associated with therapeutic response.The effect of CYP2C9*3/*3 is more pronounced compared to heterozygous and wild type homozygous genotypes as evident in many in vitro studies. As South Asians have high frequency, it would be interesting to explore potential of CYP2C9*3 as a marker for personalized therapy. Our study revealed several rare functional variants, which form eight novel and rare haplotypes of CYP2C9 (CYP2C9*63-*70). Of which, CYP2C9*64, *65, *66, *68, *69 and *70 haplotypes are South Asian-specific. CONCLUSION: Overall, we find high genetic heterogeneity within South Asians and identified South Asian-specific putative functional CYP2C9 haplotypes. High frequency of CYP2C9*3 and CYP2C9*3/*3 was observed in South Asian populations. Taken together, current study greatly enriches the knowledge of naturally occurring CYP2C9 variants and its diversity in South Asia, which are relevant to further CYP2C9-related functional research and for personalized medicine.

Diagnostic Dilemma in a 15-Year-Old Boy with Recurrent Pneumococcal Meningitis.

Recurrent bacterial meningitis is very rare phenomenon in paediatric age group. Finding the aetiology of recurrent meningitis often poses diagnostic challenge to clinicians. Among the several aetiologies, structural deficiencies at the base of skull, congenital or acquired, are readily correctable ones and hence it's imperative to find out the location of the gap in order to surgically repair it. We report the diagnostic dilemma faced while managing a 15-year-old boy with recurrent pneumococcal meningitis. Aetiology could only be found after exclusion of immunodeficiency and performing a series of imaging studies. CT cisternogram clinched the diagnosis and patient was cured successfully. A single imaging modality, be it CT/MRI scan, although proven to be better than others according to literature, might not be sufficient while finding the cause of recurrent bacterial meningitis in an immunocompetent host.